Dynamic covalent nano-networks comprising antibiotics and polyphenols orchestrate bacterial drug resistance reversal and inflammation alleviation

New antimicrobial strategies are urgently needed to meet the challenges posed by the emergence of drug-resistant bacteria and bacterial biofilms. This work reports the facile synthesis of antimicrobial dynamic covalent nano-networks (aDCNs) composing antibiotics bearing multiple primary amines, polyphenols, and a cross-linker acylphenylboronic acid. Mechanistically, the iminoboronate bond drives the formation of aDCNs, facilitates their stability, and renders them highly responsive to stimuli, such as low pH and high H2O2 levels. Besides, the representative A1B1C1 networks, composed of polymyxin B1(A1), 2-formylphenylboronic acid (B1), and quercetin (C1), inhibit biofilm formation of drug-resistant Escherichia coli, eliminate the mature biofilms, alleviate macrophage inflammation, and minimize the side effects of free polymyxins. Excellent bacterial eradication and inflammation amelioration efficiency of A1B1C1 networks are also observed in a peritoneal infection model. The facile synthesis, excellent antimicrobial performance, and biocompatibility of these aDCNs potentiate them as a much-needed alternative in current antimicrobial pipelines.


Introduction
Drug-resistant bacteria have posed an ever-increasing threat to global human health. An estimated 10 million people will die from infections caused by drug-resistant bacteria if no practical efforts to curtail bacterial resistance or new antibiotics are developed by 2050 [1,2]. Multiple mechanisms of drug resistance include target alterations, antibiotics-inactivating enzymes, membrane efflux pumps, and the formation of bacterial biofilms [3][4][5][6]. Drug combinations [7], via either physical mixing or chemical bonding, of more than one antibiotic or antibiotic with an adjuvant [8] may offer a productive strategy to achieve multiple targets or inhibit the activity of enzymes that inactivate or pump out antibiotics. The flourish of preclinical drug combinations studies has failed mainly in clinical translation due to the different pharmacokinetic properties of the drugs in combination [9]. To maximize the clinical benefit of the drug combination, various drug hybrids composing two or more pharmacophores with distinct mechanisms of action have been developed [10][11][12]. Notably, most hybrids are conjugated via chemical bonds such as ester and amide, where the drug release is strongly limited by the bacterial hydrolases [7,13]. Although dynamic chemistry may provide more feasibility for drug targeting and release via an on-demand manner, there is still a distinct lack of facile Peer review under responsibility of KeAi Communications Co., Ltd. ways to fabricate dynamic-covalent-bond-conjugated antibiotics hybrids.
Notably, dynamic covalent strategy offers enormous possibilities for generating materials using different building blocks [14][15][16]. Usually, these building blocks bond to each other via reversible dynamic covalent bonds, virtually encompassing various possible combinations, and allowing the construction of thermodynamically stable and adaptive processes owing to the dynamic interconversion of the constituents [17][18][19]. It has been witnessed that dynamic covalent strategy was applied in fields, such as drug discovery [17,20] and delivery [21], for applications ranging from cancer therapy to bacterial eradication [22][23][24]. In principle, an increase in the valency of the building blocks can lead to more complex topologies and functionalities.
Herein, we report the facile fabrication of antimicrobial Dynamic Covalent nano-Networks (aDCNs) using a multicomponent reaction between multi-amine antibiotics (A domain, e.g., polymyxins and aminoglycosides), formyl-phenyl boronic acid (B domain), and polyphenols (C domain, e.g., catechols and pyrogallols), as illustrated in Scheme 1. Multi-amine antibiotics are commonly used in the clinic. For example, polymyxins efficiently eradicate Gram-negative bacteria by disrupting the membrane integrity. However, serious side effects, such as nephrotoxicity and neurotoxicity, may also occur due to the polyamine nature of these antibiotics. Worse still, bacteria are easy to mutate and being recalcitrant to conventional antibiotics, even the last resort of therapy, polymyxins [25]. Moreover, the positively charged antibiotics interact with the negatively charged biofilm matrix, limiting the penetration of antibiotics inside biofilms and, ultimately, the killing efficacy [5]. In our strategy, iminoboronate bonds will be formed orthogonally and act as dynamic cross-linkers between antibiotics and adjuvants, forming a highly branched nano-network structure. The amine groups of antibiotics are temporally protected via the formation of iminoboronate bonds. Iminoboronate bonds have been applied as the driving force to construct a myriad of functional structures [26][27][28][29][30]. Meanwhile, iminoboronate bonds are responsive to a wide range of intracellular parameters such as low pH, high glucose/glutathione/H 2 O 2 levels, etc. [31][32][33][34][35] These aDCNs are expected to respond to a bacterial infection microenvironment with low pH and high H 2 O 2 levels [36][37][38], releasing antibiotics and polyphenols that can synergistically address bacterial drug resistance and alleviate inflammation. Besides, the nanoscale nature endows these aDCNs with the capability to inhibit the formation of biofilms or eradicate the established biofilms better [5,[39][40][41]. The temporal modification of amines on antibiotics may overcome their intrinsic cytotoxicity to normal tissues. These aDCNs could provide an efficient alternative pathway to overcome the drug resistance of bacteria and hold great potential in clinical translation.

General procedure to prepare aDCNs
Before preparing the aDCNs, various stock solutions were freshly prepared, as illustrated in Table S1. To prepare A1B1C1 nano-networks, PVP (6 mg) was dissolved in a carbonate buffer (pH 8.5, 50 mM, 2.21 mL), in which the stock solutions of 4-FPBA in DMSO (162 μL, B1) and polymyxin B in ultrapure water (300 μL, A1) were added at room temperature under a magnetic stirring (2500 rpm). Then, quercetin in DMSO (327 μL) was added dropwise at a rate of 1 drop/10 s with a final NH 2 /C--O/catechol molar ratio of 1:1:1 and a final concentration of polymyxin B of 1 mg/mL. The resulting suspension became milky upon adding polyphenols and kept being stirred at room temperature for 1 h. All other libraries were prepared essentially the same as the abovedescribed procedure. The freshly prepared nanoparticles were kept in a fridge at 4 • C before further usage and tests.

Stability of A1B1C1 nano-networks
To study the stability of aDCNs, freshly prepared aDCNs in phosphate buffer (10 mM, pH 7.4), PBS with or without 10% fetal bovine serum, were kept at room temperature for 4 weeks. The size and zeta potential of aDCNs were measured on a Zetasizer Nano ZEN3600 (Malvern, UK).
The MIC values were taken as the lowest antibiotics concentration at which bacterial growth was absent. Subsequently, the MBC values were determined by plating aliquots of suspensions with concentrations yielding no visible growth of bacteria on LB agar plates after being incubated for 24 h at 37 • C. The lowest concentration at which colony formation remained absent was taken as the MBC.

Biofilm growth inhibition
To inhibit the growth of biofilms, various formulations (A1, A1C1, and A1B1C1 in PBS at an equal A1 concentration of 64 μg/mL, 1 mL) were added to a confocal dish. The dish was kept at room temperature for 15 min statically. Then, a bacterial suspension of E. coli WL5301 (2 × 10 8 bacteria/mL, 1 mL) was added to each dish and incubated at 37 • C for another 1 h. The resulting suspension was replaced with a 2 mL LB medium containing various formulations with an equal A1 concentration of 64 μg/mL, incubated at 37 • C for 12 h. To visualize the biofilm growth, the supernatants were removed. Biofilms were stained with SYTO™ 9 for 15 min, fixed with a 4% fixative solution (Solarbio, Beijing, China), and observed on a Nikon A1 (Nikon, Japan) confocal laser scanning microscope using a 488 nm laser as excitation light and a filter of 500-550 nm. COMSTAT 2.1 ImageJ plugin was used to reconstruct the 3D images of biofilms, analyze the fluorescence intensity in each confocal plane, and construct the 3D project of the representative confocal plane at a depth of 6 μm from the biofilm bottom. Biofilms treated with PBS served as the control. Biofilm analysis: Biomass (μm 3 / μm 2 ) is the volume of biomass divided by the area of view; Roughness Coefficient measures the variability in the height of the biofilm; Thickness (μm) is the height corresponding to the layer.
In a parallel experiment, biofilms were grown in 96-well plates according to our previous protocols [43]. Briefly, various formulations (A1, A1C1, and A1B1C1 in PBS at an equal A1 concentration of 64 μg/mL, 100 μL) were added to each well of 96-well plates. The plates were kept at room temperature for 15 min statically. Then, a bacterial suspension of E. coli WL5301 (100 μL, 2 × 10 8 bacteria/mL) was added to each well and incubated at 37 • C for another 1 h. The resulting suspension was replaced with a 200 μL LB medium containing various formulations with an equal A1 concentration of 64 μg/mL, incubated at 37 • C for 12 h. After that, supernatants were removed, and biofilms were washed with PBS (200 μL × 3). The remaining biofilms were dispersed with PBS (200 μL), homogenized by a pipet, serially diluted, and spread on LB agar plates. The bacterial colony grown overnight on the agar plates were recorded and compared.
To visualize the morphologies of the biofilms after various treatments, biofilms were gently rinsed with sterile water to remove unattached cells, fixed in 4% tissue fixative fluid for 15 min, dehydrated in a series of ethanol solutions (30-100%), and air-dried. The attached bacteria and formed biofilms on the surfaces were observed using a SU8010 microscope (Hitachi, Japan) with an accelerating voltage of 5.0 kV.

Assessment of eDNA release
For the analysis of eDNA, various formulations (A1, A1C1, C1, A1B1C1 in PBS at an equal A1 concentration of 32 μg/mL) were treated with the E. coli WL5301 followed the steps in biofilm growth inhibition. After treatment for 12 h, biofilms were dispersed and centrifuged twice at 14,000 rpm for 15 min and filtered by a 0.45 μm filter to remove any remaining bacteria. To quantify the eDNA concentration, each sample was incubated with propidium iodide (PI) (finalized at 1 μg/mL) for 15 min at 37 • C. The fluorescence emission was conducted at excitation 584 nm, and the emission at 612 was recorded.
In a parallel study, the eDNA solution (10 μL) was subjected to agarose gel electrophoresis. The gels were imaged on a VersaDoc 3000 imager (Bio-Rad, USA) and fluorescence intensity was quantified using ImageJ software (version 1.8.0).

Characterization of drug-resistance genes in E. coli
E. coli WL5301 were cultured with LB supplemented with various formulations (A1, C1, A1C1, A1B1C1 in PBS at an equal A1 concentration of 16 μg/mL, 1/2 MIC) for 12 h. The culture medium was removed, and total RNA was extracted from bacteria by Trizol reagent (Invitrogen). The reverse transcription was performed using Prime-Script TM RT reagent Kit (Takara Bio Inc) to get cDNA. SYBR Green Real-Time PCR Master Mix (Thermo Fisher Scientific) was used to perform the quantitative real-time polymerase chain reaction (qRT-PCR). 16s rDNA was used as the internal control, and the 2 − ΔCt method was used to calculate the relative expression of genes (the PBS-treated group was normalized to 1). The sequences of the primers used in this study are listed in Supplementary Table S2.

Mature biofilm dispersal
To grow mature biofilms, briefly, a bacterial suspension of E. coli WL5301 (1 mL, 2 × 10 8 bacteria/mL) was added to each dish and incubated at 37 • C for another 1 h to allow the adhesion of bacteria. The bacterial suspension was removed carefully, and the dishes were washed with PBS (1 mL × 3). Then LB medium (2 mL) was added to each dish and incubated at 37 • C for 12 h. To study interactions of aDCNs with bacterial biofilms, a suspension of PBS containing A1, A1C1, and A1B1C1 at an equal A1 concentration of 64 μg/mL was added to the resultant biofilms, incubated at 37 • C for 4 h. The supernatants were removed, and the biofilms were stained with SYTO™ 9 and PI for 15 min, fixed with a 4% fixative solution (Solarbio, Beijing), and observed on a Nikon A-1 (Nikon, Japan) confocal laser scanning microscope. COMSTAT 2.1 ImageJ plugin was used to reconstruct the 3D images of biofilms, analyze the fluorescence intensity in each confocal plane, and construct the 3D project of the representative confocal plane at a depth of 6 μm from the biofilm bottom. Biofilms treated with PBS served as the control. Biofilm analysis: Biomass (μm 3 /μm 2 ) is the volume of biomass divided by the area of view; Roughness Coefficient measures the variability in the height of the biofilm; Thickness (μm) is the height corresponding to the layer.

Scavenging free radicals in vitro
The hydroxyl radical (•OH) and superoxide radical ( • O 2 ‾) scavenging activity of nano-networks were studied. Briefly, •OH was produced by a Fenton reaction between CuSO 4 and H 2 O 2 (10 μL of 2 mM CuSO 4 and 1 mL of 0.1 mM H 2 O 2 ), and different samples (10 μL, 0.5 mg/ mL) were added and incubated at 37 • C for 10 min •OH was measured by adding 3,3 ′ ,5,5 ′ -tetramethylbenzidine (TMB, 10 μL of 1 mM) to the above mixture, and their UV-Vis spectra were recorded on a UV-1900i spectrometer.
• O 2 ‾ was generated by mixing xanthine oxidase (50 μL, 0.05 U/mL) and xanthine (900 μL, 0.6 mM) in PBS at 37 • C for 30 min. Different samples (10 μL, 0.5 mg/mL) were added to the above solution and incubated for another 30 min, followed by the addition of hydroethidine (10 μL, 50 μg/mL). The amount of superoxide anion concentration was determined by measuring the fluorescent intensity of ethidium (the oxidation product of hydroethidine by superoxide radical) using a Hitachi F-4600 fluorescence spectrophotometer (Tokyo, Japan) at 470 nm excitation and emission at 610 nm.

Scavenging intracellular reactive oxygen species
RAW 264.7 cells were seeded in 12-well plates (10 5 cells/well) and were cultured overnight at 37 • C for 12 h. The cells were then treated with 1 mM tert-butyl hydroperoxide (TBHP) for 4 h. After being washed with PBS three times, the cells were treated with nano-networks or other formulations for another 24 h. After being stained with H 2 DCFDA and DAPI, the cells were washed repeatedly with PBS and were imaged using a fluorescence microscope. The fluorescence was measured at 527 nm with a multi-well plate reader via an excitation at 492 nm.

In vitro anti-inflammation
BMDM cells were used to study the anti-inflammation of different formulations. The BMDM cells (2 × 10 5 cells/well) were seeded in 12well plates and incubated overnight. Cells were stimulated by LPS (5 μg/mL) for 1 h and treated with different formulations. The cells were cultured for another 24 h, and the supernatant was used for ELISA analysis according to the user's guide.

Hemolysis rate evaluation
Fresh mice blood (1 mL) was suspended in PBS (10 mL), centrifuged at 4000 rpm for 15 min, and washed with PBS (10 mL × 5) to separate red blood cells (RBCs). Then, the RBCs were resuspended in PBS (10 mL) at a volume concentration of 4%. Various formulations in PBS (100 μL) at different concentrations (64-2048 μg/mL) were added to RBC suspension in PBS (4%, 100 μL). To evaluate the hemolysis of aDCNs, the resultant RBC suspension (400 μL) was mixed with PBS containing A1, A1C1, or A1B1C1 with A1 concentration ranging from 0 to 1000 μg/mL, incubated at 37 • C for 3 h and then centrifuged at 3000 rpm for 15 min. The absorbance of the supernatant was measured at 545 nm on a plate reader. RBCs treated with Triton-X and PBS were used as the positive and negative controls, respectively. The hemolysis was calculated using the following equation: Where OD test , OD neg , and OD pos are the OD values of samples, the negative control, and the positive control, respectively.
After 2 h, mice were intraperitoneally injected with PBS (100 μL, group A, control), A1 in PBS (100 μL, group B), C1 in PBS (100 μL, group C), A1C1 in PBS (100 μL, group D), or A1B1C1 in PBS (100 μL, group E) at an A1 dose of 2.5 mg/kg. The blood was collected through the eye of mice on day 2 and day 5 post-treatments, cytokines in blood were quantified by ELISA kits according to the user guide, and the main blood parameters were measured. After that, PBS (3 mL) was intraperitoneally injected into each mouse, abdomens were massaged for 3 min, and lavage fluids were collected. Part of the peritoneal fluid was serially diluted and spread on LB agar plates for CFU counting. The other part of the peritoneal fluid was used to analyze the cytokines according to the user guide. The major organs (heart, liver, spleen, lung, kidney) were fixed in 10% formalin, decalcified, embedded in paraffin, and subjected to hematoxylin and eosin (H&E) staining.

Catheter implant infection model
The implant infection model was constructed following the previous publications [44][45][46]. Briefly, ICR mice (female, 20-25 g) were anesthetized, shaved, and disinfected. The back of the mice was dissected to expose subcutaneous layers in a sterile environment. Subsequently, sterile thermoplastic polyurethane catheters (5 mm × 5 mm) were inserted, and the wound was sutured. E. coli WL5301 in PBS (10 6 bacteria mL − 1 ) was injected onto the implants subcutaneously. Two days later, the infected mice were randomly allocated into four groups (12 mice per group): PBS (100 μL, control), A1 in PBS (100 μL), C1 in PBS (100 μL), A1C1 in PBS (100 μL), or A1B1C1 in PBS (100 μL) at an A1 dose of 2.5 mg/kg were injected into the infection area of each mouse.
The infection area of each mouse was monitored post-surgery. Three days after different treatments, six mice per group were euthanized using pentobarbital for CFU enumeration and cytokine analysis. All other mice were euthanized at day 7 post-treatments, the blood was collected through the eye of mice after 24 h, cytokines in blood were quantified by ELISA kits according to the user guide, and the main blood parameters were measured. The implants and would tissues were collected for CFU enumeration and cytokine determination. The major organs (heart, liver, spleen, lung, kidney) were fixed in 10% formalin, decalcified, embedded in paraffin, and subjected to H&E staining.

Statistical analysis
All data are shown as mean ± s.d. from at least triplicate conditions unless otherwise indicated. Each experiment was repeated at least three times independently unless otherwise indicated. Statistical analyses included two-sided unpaired Student's t-test and one-way ANOVA. The P values of less than 0.05 were considered to indicate the statistical significance and are labeled in the results. Statistical analyses were done with GraphPad Prism 8 and Microsoft Excel 2019 software.

Iminoboronate bonds drive the formation of aDCNs
Our efforts to prepare the aDCNs started by mixing polymyxin B1 (A1) and 2-formylphenylboronic acid (B1) in a carbonate buffer (pH 8.5). Surprisingly, when the quercetin (C1) was added dropwise, a brown flocculent precipitate was formed immediately, even under vigorous magnetic stirring. We then used polyvinylpyrrolidone (PVP) as an adjuvant to stabilize the as-formed networks (Table S1). PVP is a water-soluble and biocompatible polymer widely used in nanoparticle preparation [47]. The introduction of PVP could facilitate the stabilization of the nanoparticle surface and regulate the reaction rate via hydrogen bonding with polyphenols. We found that the fine nano-networks were formed within minutes when we mixed the four components (Movie S1). Instead, a blurry suspension was observed when mixing A1 with C1 in the absence of the B domain (Movie S2). Within 1 h, the suspension became transparent, demonstrating that no nano-networks were formed without the B domain. The as-prepared A1B1C1 nano-networks had a hydrodynamic size of around 136 nm with a narrow distribution (polydensity index, PDI ~ 0.147) and a negatively charged surface (Fig. S1). A1, B1, and C1 were selected due to the following reasons. First, polymyxins (A1) are considered the last resort of therapy to treat multi-drug-resistant bacteria. While the highly dense positive charge of polymyxins often leads to nephrotoxicity and neurotoxicity. Therefore, this study aims to find a facile way to prevent the emergence of drug resistance and its side effects while maintaining its bactericidal efficacy. Second, B1 and C1 are the simplest structure in their corresponding libraries. Third, in our previous study, quercetin (C1) was found efficient in preventing the development of polymyxin drug resistance in Gram-negative bacteria [48].
This success in the fabrication of nano-networks encouraged us to investigate the combinations of various domains, as illustrated in Scheme 1. Random combinations of various domains offered nanonetworks with diameters ranging from 50 to 200 nm, PDIs less than 0.5, and negatively charged surfaces (Fig. 1). In our study, thirty DCNs in total were prepared. These results collectively demonstrate the potential of this methodology in fabricating antibiotics-containing DCNs in a facile way. To further demonstrate the power of this methodology, DCNs composing other amine-containing (bio)molecules and polyphenols are being developed in our lab for various applications.
We then used A1, B1, and C1 as the models to elucidate the chemical bond formation during nano-networks fabrication (Fig. 2a). As determined by solid-state nuclear magnetic resonance (NMR) spectra (Fig. 2b), the peaks of B1 and C1 at chemical shifts above 10 ppm disappeared when the A1B1C1 nano-networks formed. Similar results were observed in the solution-state NMR spectra (Fig. S2). Also, the chemical shift of B1 in 11 B NMR spectra at 26.94 ppm was replaced with 1.72 ppm in A1B1C1 nanonetworks (Fig. 2c), suggesting the transformation from phenylboronic acid to boronate. Besides, the absorption peak of C1 redshifted to 401 nm in A1B1C1 nanonetworks (Fig. 2d). Furthermore, the stretching vibration of C--O occurred at 1640 cm − 1 and the stretching vibration of B-O occurred at 1330 cm − 1 of B1 disappeared after the reaction in their corresponding Fourier-transform infrared (FT-IR) spectra. Meanwhile, the vibration of C--N occurred at 1600 cm − 1 and vibration of B-O-C occurred at 1008 cm − 1 were detected in the asformed nano-networks (Fig. 2e). These results collectively suggest that imino-boronate bonds are formed during the fabrication of nanonetworks.

Nano-networks characterizations
Then we characterized the formed nano-networks. The as-formed nano-networks were remarkably stable, and no notable variations in size and zeta potential of nano-networks were observed over the 4-week observation period in phosphate buffer (PB), phosphate-buffered saline (PBS) with or without fetal bovine serum (FBS) (Fig. 3a, 3b, and S3). Whereas A1 alone in solution was not stable due to its peptide nature, as demonstrated by the fact that A1 becomes negatively charged in solution (Fig. 3b). In the absence of B1, A1C1 turned brownish yellow within 24 h incubation in an ambient oxygen atmosphere at room temperature (Fig. 3c), probably due to the oxidation of C1. By contrast, a minor color change was noticed for the A1B1C1 nano-networks under the same conditions (Fig. 3c). UV-Vis absorption spectra (Fig. S4) also demonstrate the oxidation of C1 in solution. On the contrary, A1B1C1 nanonetworks were negligibly affected by the ambient oxygen atmosphere and temperature, suggesting the formation of boronate bonds could stabilize the catechol structure in C1 and prevent it from being oxidized (Fig. S5). Of note, nanoformulations also enhanced the thermal stability of A1, as demonstrated in the thermogravimetric test (Fig. S6). Our results demonstrated that the addition of B1 and C1 domains could neutralize the positive charge on A1, which may facilitate the stability of A1 and alleviate its cytotoxicity caused by its highly dense positive charge. The diameters of nanonetworks changed significantly and rapidly when exposed to an acidic and/or H 2 O 2 environment (Fig. S7), likely due to the break of imino-boronate bonds. Further characterizations showed that the A1B1C1 nano-networks have a spherical morphology and elements such as carbon, nitrogen, oxygen, and boron homogenously distributed over the networks, as illustrated in the transmission electron microscopy (TEM) (Fig. 3d) and scanning electron microscopy (SEM) images (Fig. S8). X-ray diffraction analysis (XRD) indicated that the A1B1C1 nano-networks possess an amorphous structure (Fig. 3f). And N 2 adsorption isotherm curves showed the porous nature of A1B1C1 nano-networks (Fig. 3g).

aDCNs are efficient in killing drug-resistant pathogens
With all the aDCNs in hand, we then studied the bactericidal efficacy of the aDCNs, as summarized in Table S3. Undoubtedly, PVP, B and C domains demonstrated negligible antimicrobial efficacy with MIC and MBC values higher than 500 μg/mL against the clinically isolated Escherichia coli (E. coli) and multi-drug resistant Staphylococcus aureus (S. aureus Xen36) [43,49]. The clinically isolated E. coli is a polymyxin-resistant strain with a MIC value of 32 μg/mL. The encapsulation of A1 in nano-networks could maintain the bactericidal efficacy of the original A1. Similar results were found in aminoglycoside-based aDCNs, which can maintain or lower the MIC and MBC values of the original aminoglycosides against S. aureus Xen36. Polyphenols can enhance the bactericidal efficacy of conventional antibiotics as an adjuvant due to their performance in downregulating the expression of drug-resistance genes [50]. To elucidate the mechanisms, we studied the expression of representative drug-resistant genes, such as plasmid-mediated colistin resistance gene (mcr-1) [51], aminoglycoside resistance gene (str-A) [52], and tetracycline resistance gene (tet-A) [52], after the treatment of A1B1C1 nano-networks via quantitative reverse transcriptase-polymerase chain reaction (qRT-PCR) (Fig. S9). These drug-resistance genes were notably downregulated after being treated with A1B1C1 nano-networks. Notably, the downregulation of drug-resistance genes did not lead to notably lower MIC and MBC values against bacteria, likely due to the loaded drugs not ultimately released, which was also observed in our previous studies [42,53].

A1B1C1 nano-networks inhibit the formation of E. coli biofilms and eradicate the pre-formed biofilms
Next, the efficacy of aDCNs in inhibiting biofilm formation and eradicating mature biofilms was investigated. A biofilm is a cluster of bacteria and their self-produced extracellular polymeric matrixes [54][55][56]. Biofilm matrixes protect the embedded inhabitants from microenvironmental variations and antibiotic exposure [57][58][59]. In this regard, biofilms account for over 80% of bacterial infections and contribute significantly to the drug resistance of bacteria [5,41]. Although various nanotechnology-based agents have been developed to combat bacterial biofilms, it is still challenging to eliminate the biofilms formed by drug-resistant bacteria, especially Gram-negative strains [39]. Two strategies, biofilm inhibition and dispersion [60], are commonly used. Normally, biofilm inhibition was realized by modifying the surfaces of substrates with superb hydrophilic polymers such as polyethylene glycol (PEG) or polyzwitterions [61,62]. We first investigated the biofilm inhibition ability of our A1B1C1 nano-networks using the clinically isolated E. coli WL5301 as a model strain. There were considerable E. coli biofilms formed on the phosphate-buffered saline (PBS)-treated surfaces. The treatments of A1 and A1C1 notably inhibited the growth of biofilms, as demonstrated by their corresponding confocal laser scanning microscopy (CLSM) images, reconstructed 3D projections, and quantified fluorescence intensity (Fig. 4a, 4b). The thickness and biomass of the biofilms treated with A1 and A1C1 in solution were significantly reduced (Fig. 4c). A1B1C1 nano-networks achieved the best biofilm formation inhibition efficacy, as we observed from the quantified biomass, roughness coefficient, and thickness of the biofilms after various treatments ( Fig. 4d and e). Alternatively, the biofilm formation inhibition efficacy of the various formulations was also investigated via the colony-forming unit (CFU) counting method, as demonstrated in Fig. 4g, where a concentration-dependent biofilm inhibition performance was observed for all the tested formulations. At an equal A1 dose, A1B1C1 nano-networks exhibited superior biofilm inhibition efficacy compared to A1 or A1C1 in solution. At an A1 concentration of 64 μg/mL, A1B1C1 nano-networks could completely inhibit biofilms' growth. Similar results were observed via the SEM images of E. coli after the treatment of A1B1C1 nano-networks (Fig. S10). We then analyzed the gene expression of fimH [63], the major detriment of type 1 fimbriae for adherence. The treatment of A1B1C1 networks could notably downregulate the expression of the fimH gene in the E. coli biofilms (Fig. S11). Moreover, A1B1C1 nano-networks also prevent eDNA production in E. coli biofilms (Figs. S12 and S13). These results collectively suggested that our A1B1C1 nano-networks can prevent biofilm formation by killing bacteria, inhibiting bacterial adhesion toward a substratum, and eDNA generation. Different from the conventional mechansim of using hydrophilic polymer-coated surface to prevent the attachment of bacteria for a long term, our strategy may provide a solution to kill bacteria and prevent the initial biofilm formation.
To eliminate the pre-formed E. coli biofilms, nano-networks were mixed with 1-day-old biofilms, and then the biofilms were stained with SYTO9/PI for CLSM observation. As illustrated in Fig. 5, the treatments of A1 and A1C1 in solution partially dissociated the mature biofilms, and numerous dead (red) bacteria were observed (Fig. 5a-c). Also, A1C1 in solution demonstrated better efficacy in biofilm dispersal than A1 in solution, possibly due to the adjuvant effect of C1. Meanwhile, A1B1C1 nano-networks demonstrated the best biofilm elimination performance, as we observed from the quantified biomass, roughness coefficient, and thickness of the biofilms after various treatments (Fig. 5d and e).In a parallel study, we also quantified the CFU of biofilms after various treatments. A1B1C1 nano-networks are far better than A1 and A1C1 in eliminating biofilm bacteria, especially at a relatively lower A1 concentration. For instance, neither A1 nor A1C1 in solution efficiently killed bacteria embedded in biofilms at an A1 concentration of 32 μg/ mL. Meanwhile, A1B1C1 nano-networks achieved 90% killing efficacy at the equal A1 concentration. Of note, a 3-log reduction in CFU was achieved when treating the biofilms with A1B1C1 nano-networks at an A1 concentration of 128 μg/mL (Fig. 5g), suggesting their potential in clinical translation. Similar results were also observed via the SEM images of E. coli after the treatment of A1B1C1 nano-networks (Fig. S14). BMDMs after various treatments. (f) IL-10 expression in the BMDMs after various treatments. ns represents no significant difference. ns represents no significance, *p < 0.05, **p < 0.01, ***p < 0.001, and ****p < 0.0001, one-way ANOVA.

A1B1C1 nano-networks alleviate ROS and cellular inflammation
Polyphenols serve as useful anti-inflammatory adjuvants because they scavenge ROS, thus terminating the subsequent chain reactions on/ in cells. To determine the ROS scavenging efficiency of our A1B1C1 nano-networks, we first tested the clearance of •OH and •O 2 − in the presence of A1B1C1 nano-networks using UV-Vis and fluorescence spectrometry. As shown in Fig. 6a, 6b and Fig. S15, C1 and A1B1C1 nano-networks demonstrated similar and highest •OH and •O 2 − scavenging efficiency, suggesting that the incorporation of C1 in nanoformulation will not affect its ROS scavenging ability. The advantage of our A1B1C1 nano-networks was that the ROS scavenging ability of C1 was maintained mainly even after being kept at room temperature for 24 h, likely since A1B1C1 nano-networks could stabilize C1 and prevent the oxidation of C1 in solution. Similar results were found in a tert-butyl hydroperoxide (TBHP)induced inflammatory macrophage model, where intracellular ROS was notably eliminated when the inflamed cells were treated with A1B1C1 nano-networks, as observed via their corresponding CLSM images (Fig. 6c). Of note, A1B1C1 nano-networks exhibited far better ROSelimination ability than C1 or A1C1 in solution, likely due to the poor solubility of C1 or A1C1 in water and subsequent poor internalization efficiency. Consistently, quantified fluorescence intensity from either CLSM images (Fig. 6d) or the microplate reader (Fig. 6e) showed the prior ROS-elimination ability of A1B1C1 nano-networks compared to all other treatments. In detail, interleukin 6 (IL-6) [64] and interleukin 10 (IL-10) [65] were selected as the representative cytokines to indicate the inflammation states of the macrophages after various treatments. Compared to other treatments, both IL-6 and IL-10 in the lipopolysaccharide (LPS)-treated bone-marrow-derived macrophages (BMDMs) were notably neutralized after the A1B1C1 nano-networks treatment ( Fig. 6f-h). These results indicated that the treatment of A1B1C1 nano-networks could efficiently regulate the inflammation of the LPS-treated macrophages, which might benefit bacterial eradication via immune intervention [66].

A1B1C1 nano-networks eliminate bacteria and inflammation in an abdominal murine infection model
Then a murine abdominal infection model was constructed using E. coli WL5301, and the infected mice were intraperitoneally injected with various treatments (Fig. 7a). A1B1C1 nano-networks demonstrated the best antimicrobial efficacy compared to all other treatments. A 2.5log reduction in CFU was achieved when the mice were treated with A1B1C1 nano-networks (Fig. 7b) compared to the PBS-treated control group. Moreover, IL-6 in the bacteria-infected mice was neutralized after the A1B1C1 nano-networks treatment, while IL-10 was notably upregulated (Fig. 7c, 7d). This regulation of inflammation cytokines is likely due to the eradication of bacteria at the infection sites [24]. Indeed, the expression of IL-6 and IL-10 did not show significant differences between the treatment of A1 and A1B1C1 on day 2 post-treatment. In contrast, significant differences appear on day 5 post-treatment, when concentration of 64 μg/mL ns represents no significance, *p < 0.05, **p < 0.01, ***p < 0.001, and ****p < 0.0001, one-way ANOVA. most bacteria have been eradicated. Moreover, H&E staining of major organs after treatment with various treatments indicated the lack of obvious pathological abnormalities (Fig. S16). Similarly, major blood parameters of mice treated with A1B1C1 nano-networks showed minor variations compared to PBS (Fig. S17). It therefore indicated the desired biocompatibility of NCs with minimal systemic toxicity.

A1B1C1 nano-networks eliminate biofilm-associated implant infection
We then constructed a subcutaneous catheter implant infection model and evaluated the bactericidal efficacy of our A1B1C1 nanonetwork in eradicating bacteria and alleviating inflammation, as illustrated in Fig. 8a. The treatment of A1B1C1 nano-networks showed the property to accelerate wound closure, superior compared to all other treatments (Fig. 8b). Besides, A1B1C1 nano-networks demonstrated the best antimicrobial efficacy compared to all other treatments. A 3.5-log reduction in CFU in the catheters and wound tissues was achieved when the mice were treated with A1B1C1 nano-networks (Fig. 8c) on day 3 post-treatment compared to the PBS-treated control group. On day 7, A 5-log reduction in CFU in the catheters and wound tissues was achieved with A1B1C1 nano-networks (Fig. 8d) on day 7 posttreatment. The expression of IL-6 was largely neutralized after the A1B1C1 nano-networks treatment, while IL-10 was notably upregulated (Fig. 8e and f). Similarly, major blood parameters of mice treated with A1B1C1 nano-networks showed minor variations compared to PBS (Fig. S18). And the body weight of mice was negligibly affected by all the treatments (Fig. S19). Besides, H&E staining of major organs after treatment with various treatments indicated the lack of obvious pathological abnormalities (Fig. S20). These results collectively suggested that our A1B1C1 nano-networks could efficiently eradicate the bacteria and alleviate inflammation in the implant infection model without notable side effects to the normal tissues.

A1B1C1 nano-networks ameliorate the cytotoxicity of A1
Typically, the clinical application of polymyxins is primarily plagued by their cytotoxicity caused by their dense positive surface charge. Various strategies, such as encapsulating or entangling polymyxins into nanoparticles, have been developed to minimize cytotoxicity and target the infection sites [67]. In our design, the amino groups on the polymyxin or other antibiotics are temporally protected by the imine bonds. Therefore, the A1B1C1 nano-networks have negatively charged surfaces that allow the stability of nano-networks in solution and minimize the cytotoxicity caused by positively charged materials [68]. To evaluate the cytotoxicity of our A1B1C1 nano-networks, firstly, their hemolysis rate was evaluated using murine red blood cells. Negligible hemolysis was observed even though the red blood cells were treated with A1B1C1 nano-networks at an A1 concentration of 500 μg/mL. In contrast, A1 in solution induced significant hemolysis of red blood cells ( Fig. 9a and b). Similarly, in human embryonic kidney (HEK293) cells, free A1 in solution showed remarkable cytotoxicity, whereas A1B1C1 nano-networks could significantly alleviate the cytotoxicity of free A1 (Fig. 9c). Therefore, our A1B1C1 would efficiently alleviate the original cytotoxicity of free A1 while maintaining or strengthening its bactericidal efficiency.

Conclusion
In summary, we have constructed the aDCNs composing the aminecontaining antibiotics, acylphenylboronic acid, and polyphenols via a facile one-pot reaction in minutes. The iminoboronate bond in aDCLs drove the formation of aDCLs and facilitated the stabilization of aDCNs. Meanwhile, the nanoformulation of aDCLs notably alleviated the intrinsic cytotoxicity of the multiple amine-containing antibiotics and prevented the polyphenols from being oxidized. The representative A1B1C1 nano-networks demonstrated superior antimicrobial efficacy compared to the free antibiotics in preventing biofilm formation and eliminating mature biofilms. Also, A1B1C1 nano-networks orchestrated bacterial killing and inflammation alleviation in a murine peritoneal infection model. This facile methodology of preparing aDCLs and their efficiency in therapy may provide a much-needed alternative for treating drug-resistant bacteria-induced infections in this 'post-antibiotics' Quantified hemolysis rate of red blood cells after various treatments at different concentrations. Red blood cells treated with PBS served as the negative control, and the cells treated with water served as the positive control (100% hemolysis rate). (c) Viability of HEK293 cells after various treatments at different concentrations. The cell viability of HEK293 treated with PBS was set as 100%. **p < 0.01, ***p < 0.001, one-way ANOVA. era.

Ethics approval and consent to participate
The animal experimental protocols were reviewed and approved by the Institutional Animal Care and Use Committee, Wenzhou Institute, University of Chinese Academy of Sciences (No. WIUCAS21071223).

Declaration of competing interest
Y.L., Y.F.Li, and H.C. declare the following competing interests: a patent has been filed covering the materials and methods reported herein. All other authors declare no other competing interests.